Mr. Anuj Kulshreshtha Profile

Anuj Kulshreshtha

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Proceedings Article | 7 May 2016 Paper

Estimation of turbidity in coastal waters using satellite data

Anuj Kulshreshtha, Palanisamy Shanmugam

Proceedings Volume 9878, 987805 (2016) https://doi.org/10.1117/12.2223544

KEYWORDS: Water, Satellites, Remote sensing, Ocean optics, Error analysis, Oceanography, Coastal modeling, Near infrared, Visible radiation, Satellite imaging

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The assessment of water clarity of any regional water body is particularly important from ecological and water quality perspectives, especially in the regions which are highly influenced by sediment run-off and seasonal fluctuations in turbidity. The ocean colour remote sensing has played a significant role in monitoring the turbidity level in marine and inland water bodies. However, algorithms to accurately estimate the turbidity in such optically complex waters are scarce or limited by high level of uncertainty due to various issues. The present study proposes a simple, two band algorithm to estimate turbidity in both turbid and clear waters. It was found that the band ratio of remote sensing reflectance (R_rs(670)/R_rs(670)+R_rs(555)) represents the proxy of TSS (Total suspended sediment) and therefore, positively correlates to turbidity. The new algorithm is based on the assumption that light reflected in these two vital bands contains the essential information regarding the total suspended matter in the water column. The statistical results showed that the percent mean relative error between the predicted turbidity and the measured turbidity was within ±20%. To further demonstrate the robustness of the present algorithm, the spatial grid contours for the measured and the predicted turbidity was generated for the month of January 2014, August 2013 and May 2012 for the coastal waters in Bay of Bengal (Point Calimere, located in the southeast coast of India). The close consistency between the predicted and measured turbidity spatial patterns revealed that the present algorithm can be applied with high confidence to predict turbidity in both coastal and inland waters.

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